Predictors of problem gambling remission in adults: A Canadian longitudinal study.
Bibliographic record
Abstract
OBJECTIVE: Remission from problem gambling (PG) continues to be a priority of clinicians and researchers. Data from cross-sectional studies indicate that some correlates are more predictive of PG, and existing longitudinal studies have exclusively examined risk factors that predict emergence of PG. This study's objective is to fill in the remaining pieces of the puzzle by identifying factors that might facilitate remission from PG. METHOD: = 468) were assessed on a series of modifiable gambling, psychosocial, mental health, and substance use variables. A forward stepwise logistic regression was conducted to identify the strongest predictors of remission from PG at follow-up. A Least Absolute Shrinkage and Selection Operator regression was also conducted to confirm the most relevant predictors. RESULTS: Out of 75 candidate variables, 10 were retained by the regression model. Two were related to cessation of specific gambling activities, two were related to gambling motivations, two were psychosocial in nature, two were related to substance use while gambling, and one was related to remission from a mental health disorder. The final and strongest predictor was PG severity at baseline. CONCLUSIONS: Although PG remission predictors were mostly gambling-related, psychosocial aspects may also be targeted by stakeholders aiming to reduce PG. Ceasing to use tobacco while gambling and diversifying leisure activities may be promising targets. Other mental health and substance use predictors may still possibly be relevant, but only for a subset of people with PG. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".